基于加权双谱特征的HCM心音分类新方法。

IF 2.4 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Fang Yu, Huang Zhiyuan, Leng Hongxia, Dongbo Liu, Wang Weibo
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引用次数: 0

摘要

肥厚性心肌病(HCM),包括阻塞性和非阻塞性HCM,可导致青少年和运动员心脏骤停。通过不同类型HCM的听诊进行早期诊断和治疗,可以预防恶性事件的发生。然而,鉴别与左室流出道压力梯度差异相关的HCM病理信息具有挑战性。为了解决这一问题,提出了一种基于加权双谱特征的心音分类方法。预处理首先用于去除HS采集过程中的背景噪声。然后,计算双谱等高线图,提取56维特征来表示HCM的病理信息。其次,提出了一种自适应阈值加权互信息方法进行特征选择和加权融合。最后,建立CNN-RF分类器模型,自动识别不同类型的HCM病例。使用正常和两种类型HCM HSs的临床数据集进行验证。结果表明,该方法具有较好的分类效果,分类准确率达到94.4%。为临床诊断年轻HCM患者提供了可靠的参考依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new HCM heart sound classification method based on weighted bispectrum features.

Hypertrophic cardiomyopathy (HCM), including obstructive HCM and non-obstructive HCM, can lead to sudden cardiac arrest in adolescents and athletes. Early diagnosis and treatment through auscultation of different types of HCM can prevent the occurrence of malignant events. However, it is challenging to distinguish the pathological information of HCM related to differential left ventricular outflow tract pressure gradients. To address this issue, a classification method based on weighted bispectrum features of heart sounds (HSs) is proposed for efficient and cost-effective HCM analysis. Preprocessing is first applied to remove background noise during HS acquisition. Then, the bispectrum contour map is calculated, and 56-dimensional features are extracted to represent the pathological information of HCM. Next, an adaptive threshold weighting mutual information method is proposed for feature selection and weighted fusion. Finally, the CNN-RF classifier model is built to automatically identify different types of HCM cases. A clinical dataset of normal and two types of HCM HSs is utilized for validation. The results show that the proposed method performs well, with a classification accuracy reaching 94.4%. It provides a reliable reference for HCM diagnosis in young patients in clinical settings.

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来源期刊
CiteScore
8.40
自引率
4.50%
发文量
110
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